Cuda Programming in EECS

Some EECS Linux systems have NVIDIA GPUs capable of running CUDA applications. In addition to having a compatible card, a special driver is also needed for CUDA. Below are some tips on how to get more information on CUDA capabilities and programming in EECS.

You can discover whether your computer has a CUDA-capable NVIDIA card by checking the PCI-connected hardware with the lspci command:

jruser:hydra9 ~> lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation GM107 [GeForce GTX 745] (rev a2)
01:00.1 Audio device: NVIDIA Corporation Device 0fbc (rev a1)

In this example, the system has a NVIDIA GeForce GTX 745 video card. NVIDIA provides more information on which cards have CUDA capabilities and at what level.

On EECS IT-supported systems, the most recent NVIDIA's CUDA software is installed in /usr/local/cuda. Some older versions may be also available in /usr/local with version numbers such as /usr/local/cuda-10.1.

If your system supports CUDA, you may want to start by adding /usr/local/cuda/bin to your shell's PATH variable. This can be done in your shell initialization files, e.g. by adding the line export PATH=“$PATH:/usr/local/cuda/bin to your .zshrc file. If you are using default EECS shell initialization files, you will likely also have a .zsh.path where you can alter your default PATH.

For general information on CUDA programming, please see:

If you are familiar with the Linux pkg-config command, you can use it to add the proper compiler and linker options to your gcc command-line and makefiles. To see what CUDA versions are supported via pkg-config try:

jruser:hydra9 /usr/local> pkg-config --list-all | grep cuda
cuda-10.1                 cuda - CUDA Driver Library
cuda-10.2                 cuda - CUDA Driver Library
cudart-10.2               cudart - CUDA Runtime Library
cudart-10.1               cudart - CUDA Runtime Library

To get the compiler flags for CUDA version 10.2, for example, you can run:

jruser:hydra9 /usr/local> pkg-config --cflags cuda-10.2

For more information, read the manual page for pkg-config and the documentation linked above.

A lot of software packages integrate with CUDA-capable GPUs. Some of them are installed by default on EECS systems, however licensing restrictions do not always allow EECS IT to install a package for every user on a system. For example, NVIDIA cuDNN requires end-users to agree to a developer license agreement.

In general, you should be able to download your own copies of software that is not available on EECS IT systems and install it in your Linux home directory. For Python modules, use a Python VirtualEnv. If you need a specific library or software product for a course, please contact EECS IT to discuss options. Further reading:

Contact EECS IT Support if you need software for a course.

If you have determined that your system includes CUDA-capable GPU, you can use the deviceQuery command to find out more about such as its “CUDA Capability” version, number of CUDA cores, etc :

jruser:hydra9 ~> /usr/local/cuda/samples/bin/x86_64/linux/release/deviceQuery
/usr/local/cuda/samples/bin/x86_64/linux/release/deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 745"
  CUDA Driver Version / Runtime Version          10.2 / 10.2
  CUDA Capability Major/Minor version number:    5.0
  Total amount of global memory:                 4036 MBytes (4231725056 bytes)
  ( 3) Multiprocessors, (128) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            1032 MHz (1.03 GHz)
  Memory Clock rate:                             900 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 2097152 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            No
  Supports Cooperative Kernel Launch:            No
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS

If your system has a CUDA-capable GPU but you are currently unable to use the CUDA toolkit, the NVIDIA driver will likely need to be installed or updated. Red Hat Linux ships with the open-source Nouveau driver for NVIDA video cards. However, while this driver supports display capabilities, it does not support CUDA programming. To enable CUDA, the commercial NVIDIA driver needs to be installed. To check what driver is installed on your system, you can use the lsmod command to list the currently running kernel modeles (drivers):

jruser:hydra9 ~> lsmod | grep -E "nvidia|nouveau"
nvidia_drm             39594  3
nvidia_modeset       1109637  6 nvidia_drm
nvidia_uvm            939731  0
nvidia              20390418  253 nvidia_modeset,nvidia_uvm
drm_kms_helper        186531  1 nvidia_drm
drm                   456166  6 drm_kms_helper,nvidia_drm
ipmi_msghandler        56728  2 ipmi_devintf,nvidia

In the above example, the “nvidia” driver is installed. On systems with Nouveau you will see something similar to this:

jruser:hydra9 ~> lsmod | grep -E "nvidia|nouveau"
nouveau              1898794  7
mxm_wmi                13021  1 nouveau
i2c_algo_bit           13413  1 nouveau
drm_kms_helper        186531  1 nouveau
ttm                    96673  1 nouveau
drm                   456166  7 ttm,drm_kms_helper,nouveau
wmi                    21636  6 dell_smbios,dell_wmi_descriptor,dell_led,dell_wmi,mxm_wmi,nouveau
video                  24538  1 nouveau

Please contact EECS IT support for help getting the NVIDIA drivers installed on your system.